FFTsurrogate: Generate surrogate data using the Fourier transform

In nonlinearTseries: Nonlinear Time Series Analysis

Generate surrogate data using the Fourier transform

Description

Generates surrogate samples from the original time series.

Usage

FFTsurrogate(time.series, n.samples = 1)

Arguments

time.series	The original time.series from which the surrogate data is generated.
n.samples	The number of surrogate data sets to generate,

Details

This function uses the phase randomization procedure for generating the surrogated data. This algorithm generates surrogate data with the same mean and autocorrelation function (and thus, the same power spectrum because of the Wiener-Khinchin theorem) as the original time series.

The phase randomization algorithm is often used when the null hypothesis being tested consist on the assumption that the time.series data comes from a stationary linear stochastic process with Gaussian inputs. The phase randomization preserves the Gaussian distribution.

Value

A matrix containing the generated surrogate data (one time series per row).

Author(s)

Constantino A. Garcia

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

Examples

## Not run: 
# generate 20 surrogate sets using as original time series
# an arma(1,1) simulation
time.series = arima.sim(list(order = c(1,0,1), ar = 0.6, ma = 0.5), n = 200)
surrogate = FFTsurrogate(time.series, 20)

## End(Not run)

nonlinearTseries documentation built on Sept. 23, 2024, 5:10 p.m.